Modeling House Price Prediction using Regression Analysis ...

1 (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 10, 2017 323 | P a g e Modeling House Price Prediction using Regression Analysis and particle swarm Optimization Case Study: Malang, East Java, IndonesiaAdyan Nur Alfiyatin Faculty of Computer Science Brawijaya University, Malang, Indonesia Ruth Ema Febrita Faculty of Computer Science Brawijaya University, Malang, Indonesia Hilman Taufiq Faculty of Computer Science Brawijaya University, Malang, Indonesia Wayan Firdaus Mahmudy Faculty of Computer Science Brawijaya University, Malang, Indonesia Abstract House prices increase every year, so there is a need for a system to predict House prices in the future. House Price Prediction can help the developer determine the selling Price of a House and can help the customer to arrange the right time to purchase a House . There are three factors that influence the Price of a House which include physical conditions, concept and location.

2 This research aims to predict House prices based on NJOP houses in Malang city with Regression Analysis and particle swarm optimization (PSO). PSO is used for selection of affect variables and Regression Analysis is used to determine the optimal coefficient in Prediction . The result from this research proved combination Regression and PSO is suitable and get the minimum Prediction error obtained which is IDR Keywords House Prediction ; Regression Analysis ; particle swarm optimization I. INTRODUCTION Investment is a business activity that most people are interested in this globalization era. There are several objects that are often used for investment, for example, gold, stocks and property. In particular, property investment has increased significantly since 2011, both on demand and property selling [1]. One of the increasing of property demand is because of high population in Indonesia.

3 Indonesian Central Bureau of Statistics states that in East Java 50% of the population of East Java classified as a young population who have age approximately at 30 years old [2]. The result of this census indicates that the younger generation will need a House or buy a House in the future. Based on preliminary research conducted, there are two standards of House Price which are valid in buying and selling transaction of a House that is House Price based on the developer (market selling Price ) and Price based on Value of Selling Tax Object (NJOP). According to Lim, et al the fundamental problem for a developer is to determine the selling Price of a House [3]. In determining the Price of home, the developer must calculate carefully and determine the appropriate method because property prices always increase continuously and almost never fall in the long term or short [4].

4 There are several approaches that can be used to determine the Price of the House , one of them is the Prediction Analysis . The first approach is a quantitative Prediction . A quantitative approach is an approach that utilizes time-series data [5]. The time-series approach is to look for the relationship between current prices and prevailing prices. The second approach is to use linear Regression based on hedonic pricing [6], [7]. Previous research conducted by Gharehchopogh, et al. [7] using linear Regression approach get 0,929 error with the actual Price . In linear Regression , determining coefficients generally using the least square method, but it takes a long time to get the best formula. particle swarm optimization (PSO) is proposed to find the coefficients aimed at obtaining optimal results [8]. Some previous researches such as Marini and Walzack [9], [10] show that PSO gets better results than other hybrid methods.

5 There are several advantages of PSO, in the small search space PSO can do better solution search [11]. Although the PSO global search is less than optimal [12], but on the optimization problem the value of the variable on the Regression equation can find a maximum solution using PSO [12], [13]. This research aims to create a House Price Prediction model using Regression and PSO to obtain optimal Prediction results. PSO is used for selection of affect variables in House Prediction , Regression is used to determine the optimal coefficient in Prediction . In this study, researchers wanted to know the performance of the developed model in time series data. Prediction House prices are expected to help people who plan to buy a House so they can know the Price range in the future, then they can plan their finance well. In addition, House Price predictions are also beneficial for property investors to know the trend of housing prices in a certain location.

6 This research is focused in Malang City, because Malang is one of tourism and urban city in East Java. II. RELATED WORK A. House Price Affecting Factors There are several factors that affect House prices. In his research Rahadi, et al. [14] divide these factors into three main groups, there are physical condition, concept and location. Physical conditions are properties possessed by a House that (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 10, 2017 324 | P a g e can be observed by human senses, including the size of the House , the number of bedrooms, the availability of kitchen and garage, the availability of the garden, the area of land and buildings, and the age of the House [15], while the concept is an idea offered by developers who can attract potential buyers, for example, the concept of a minimalist home, healthy and green environment, and elite environment.

7 Location is an important factor in shaping the Price of a House . This is because the location determines the prevailing land Price [16]. In addition, the location also determines the ease of access to public facilities, such as schools, campus, hospitals and health centers, as well as family recreation facilities such as malls, culinary tours, or even offer a beautiful scenery [17], [18]. In general, the factors affecting the House prices will be presented in Table 1. TABLE I. House Price AFFECTING FACTORS Literature Physical condition Concept Location House size Bedroom Kitchen Garage Surface area Garden Age of home Concept House Access to health facilities Access education facilities Restaurant Public transportation Scenery [15] (Limsombunchai, 2004 ) [18] (Jim and Chen, 2009) [17] (Kisilevich, Keim and Rokach, 2013) [16] (Zhu and Wei, 2013) [14] (Rahadi, et all, 2015) [19] (Bryant, 2016) B.

8 Hedonic Pricing Hedonic pricing is a Price Prediction model based on the hedonic Price theory, which assumes that the value of a property is the sum of all its attributes value [20]. In the implementation, hedonic pricing can be implemented using Regression model. Equation 1 will show the Regression model in determining a Price . Where, y is the predicted Price , and x1, x2, xi are the attributes of a House . While a, b, .. n indicate the correlation coefficients of each variables in the determination of House prices. III. DATA SET In this research, we use House Price data based on NJOP from Land and Building Tax (PBB) payment structure. Due to limited access to the data, this study used 9 houses data in time series scattered in Malang City area, within 2014-2017. Normalization of data is done by completing the empty data at a certain time with the assumption that land prices tend to change every 2 years, while building prices tend to be stable.

9 The data tabulation offer information of the houses includes: home id, address (street name), longitude-latitude, year, building area, land area, NJOP building Price (IDR/m2), NJOP land Price (IDR/m2), distance from city center(km), amount number of campuses, amount number of restaurants, amount number of health facilities, amount number of playground, amount number of schools, amount number of traditional markets or malls, amount number of worship places, and also easiness access to public transportation. The city center in this study defined as the location of the square of Malang City. The distance to city center is calculated using Google maps. Meanwhile, easy access to public transportation is calculated between radius 400 meter. The calculation of nearest objects in the certain radius using buffering techniques accessed through the site IV.

10 RESEARCH METHODOLOGY Fig. 1. Diagram flow research. Based on Fig. 1, the process of Regression Analysis and particle swarm optimization methods is described in the following section: A. Regression Analysis The Prediction model used in this research is hedonic pricing, the suitable model using Regression , with the standard formula as shown in (1). The dependent variable symbolized as Y is NJOP Price and independent variables with symbol x1- x14 consist of year, building area, land area, NJOP land Price (IDR/m2), NJOP building Price (IDR/m2), distance to center of the city, amount number of campuses, amount number of restaurants, amount number of health facilities, amount number of amusement parks, amount number of educational facilities, amount number of traditional markets, amount number of worship places, and easiness to public transportations is shown in (2). (IJACSA) International Journal of Advanced Computer Science and Applications, Vol.